1. Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification
Irene Amerini, Roberto Caldelli, Vito Cappellini, Francesco Picchioni, Alessandro Piva
Santorini,
Good Morning, I’m Francesco Picchioni and Today I present to you the “work” of the title:

2. Outline Multimedia Forensics Source Camera Identification
Digital camera acquisition process
Analysis of different wavelet denoising filters
Experimental results
Future Trends
This is the Outline of the presentation:
First we talk about scenario.
Next I introduce the Multimedia Forensic and the state of art. In the end we see methods and results.
Finally I talk about the Future Trends.
First we talk about the scenario where we
Next I introduce the Multimedia Forensic and also we see the state of art of M.F. In the end we see methods that we developed and the results that we obtain applying this.
First I will introduce the scenario process through the use of a digital camera. The second and the third sections will be devoted to the analysis of the principal techniques exploited respectively for identifying the acquisition device of digital images and for assessing the authenticity of digital images. Some experimental results, in particular for source identification, will be reported and conclusions will be provided in the last two sections.

3. Multimedia Forensics The goals of multimedia forensics are:
Forgery detection
Source Identification: determine the device that acquired an image (scanner, CG, digital camera, ...)
Source Camera Identification
Which camera brand took this picture
What model?
Specific device?
BRAND
MODEL
D40x
Nikon
image, video and audio
forensic image analysis is the application of image science and domain expertise
to interpret the content of an image or the image itself in legal matters (SWGIT-
L12
Canon
D50
Sony
S650
etc…

4. Digital Camera Acquisition Process
[Fridrich06]
Fingerprint from the acquisition process
CCD sensor imperfections
To extract PRNU we need to modelling the acquisiton process and identify it.
Because details about the processing are not always easily available (they are hard- wired or proprietary), generally is needed to use a simplified model that captures various elements of typical in-camera processing:
We can see the sensor output I where I(0) is the sensor output in the absence of noise gamma is the gamma correction factor
Teta Is a complex of independent random noise components. The multiplicative factor K is a zero-mean noise-like signal responsible for PRNU (the sensor fingerprint)‏

5. Sensor Imperfections
defective pixels: hot/dead pixels (removed by post-processing)‏
shot noise (random)
pattern noise (systematic)
Fixed Pattern Noise: dark current (exposure, temperature) suppressed by subtracting a dark frame from the image.
Photo Response Non Uniformity: caused by imperfection in manufacturing process
slightly varying pixel dimensions
inhomogeneities in silicon wafer.
Sensor noise is compose by many components:The two main component are Shot Noise (a typical random noise) and Pattern Noise ( a systematic noise-like component) We can decompose Pattern Noise into:FPN: compose by dark current (exposure, temperature) that is generally suppressed subtracting dark frame from image And PRNU caused by imperfection in manufacturing process (that is suppressed only in particular sensor, not in digital camera, with a complex technic called Flat Fielding) that is due to slightly varying pixel dimensions and inhomogeneities in silicon wafer.
We can use PRNU like Fingerprint because is embedded into every image and is unique for each digital camera;
When the imaging sensor takes a picture of an absolutely evenly lit scene, the resulting digital image will still exhibit small changes in intensity among individual pixels. These errors include sensor’s pixel defects and pattern noise this last has two major components, namely, fixed pattern noise and photo response non-uniformity noise (PRNU). The most important component of PRNU is the pixel non- uniformity (PNU), which is defined as different sensitivity of pixels to light. The PNU is caused by stochastic inhomogenities present in silicon wafers and other imperfections originated during the sensor manufacturing process. Finally the noise component to be estimated and to be used as intrinsic characteristic of the sensor (fingerprint) is the PNU.
Template deterministoco impresso sopra l’immagine
PNU (pixel non uniformity)‏
Low frequency defects: rifrazione della luce, particelle di polvere
PRNU as Fingerprint
unique for each sensor

6. Digital Camera Model Additive-multiplicative relation
noisy image
noise free image
PRNU
Additive-multiplicative relation
To extract PRNU we need to modelling the acquisiton process and identify it.
Because details about the processing are not always easily available (they are hard- wired or proprietary), generally is needed to use a simplified model that captures various elements of typical in-camera processing:
We can see the sensor output I where I(0) is the sensor output in the absence of noise gamma is the gamma correction factor
Teta Is a complex of independent random noise components. The multiplicative factor K is a zero-mean noise-like signal responsible for PRNU (the sensor fingerprint)‏
Find , F denoising filter

7. Digital Camera Identification
fingerprint estimation
taken by the same camera A
This is the process to exctract fingerprint first we take N images from a camera and for each image we apply the selected Denoising Filter to obtain DEnoised Images; next we subtracting from each Noisy Image the respective Denoised one to get the PRNU and finally averaging them we get the FingerPrint of the camera.
PRNU
camera A

8. Digital Camera Identification
fingerprint detection
The test image imm(k) is taken by camera A?
camera A
Finally to identify what camera has taken that image we need to exctract PRNU from that image as done before and then we performe a correlation between this PRNU and all the available Fingerprints. The fingerprint whose correlaction is higher than predefined threshold is supposed to be the camera that shoot the image.
imm(k) is taken by camera A

9. Digital Camera Identification
denoising filter
The digital filter has an important role for PRNU extraction!
Comparison and analysis of two denoising filters:
Previously used Mihçak Filter [1] additive noise model ‏
Novel Argenti-Alparone Filter [2] signal-dependent noise model
Fingerprint estimation from N images (no smooth images)‏
Fingerprint detection: correlation; given an image we calculate the noise pattern and then correlated with the known reference pattern from a set of cameras.
Decision: threshold, Neymann Pearson criterion FAR=10^-3
Mihack filter usato nei lavoro di Fridrich per la stima del PRNU
Argenti specke noise removal (SAR images)
Basato su modello di rumore solo additivo (modello + semplice)
Idea: usare un filtro basato su un modello di rumore + complesso: signal dependent cioè……I=,….
Modello paragonabile a quello del processo di acquisizione di un digital camera: uguale quando alpha=1
Modello + generico e puà essere ridotto al modello del processo di acquisizione
Modello + complesso
To extract the PRNU (fingerprint) we generally used denoising filtering in particulary in our analysis we have compare:
A basic low pass filter, used like lower bound performance
A mihcak Filter
A Argenti-Alparone Filter
All of this are filter based on Wavelet domain and different noise model. The assumption to apply our techniques is to have a camera available or N images taken by the camera
[1] K. Ramchandran M. K. Mihcak, I. Kozintsev, “Spatially adaptive statistical model of wavelet image coefficients and its application to denoising”, 1999.
[2] L. Alparone F. Argenti, G. Torricelli, “Mmse filtering of generalised signal-dependent noise in spatial and shift-invariant wavelet domain“, 2005.

10. Mihcak’s Filter additive noise model (AWGN)
spatially adaptive statistical modelling of wavelet coefficients
4 level DWT (Daubechies)
MAP (Maximum A Posteriori) approach to calculate the estimate of the signal variance
Wiener filter in the wavelet domain
LL subband
For each detail subband
Coeff.

11. Argenti’s Filter signal-dependent noise model
The parameters to be estimated are:
and
On homogeneous pixels, log scatter plot regression line and then MMSE filter in spatial domain.
MMSE (minimum mean-square error) filter in undecimated wavelet domain
estimate
noise free image noisy image
stationary zero-mean
uncorrelated random process
electronics noise (AWGN)
For each detail subband
LL subband
Noise estimate
Iterative estimate
Minimizzazione lineare locale errore quadratico medio
Prima stima di alpha e sigmau: si riduce il carico computazionale
Da test fatti il raffineanto della stima non incide nei risultati nel caso della source identification magari nello speckle ha più senso

12. Results- denoising filter comparison
10 digital cameras.
Data set:
training-set to calculate the fingerprint: 40 images for each camera.
test-set: 250 images for each camera.
A low pass filter (DWT detail coefficients are set to zero) is used to provide a performance lower bound.
Mihçak filter: 99.09%
Argenti filter: 96.61%
Low Pass filter: 84.44%
The LP filter has the worst behaviour as obviously expected.
The other two filters showed a comparable behaviour: the FRR has the same ored of magnitude
Argenti’s filter has a significative lower FRR for Samsung and Olympus
In the other case does not exhibit a considerable improvement in the results
Because the filter depends on the reliability of the parameters estimation

13. Results- denoising filter comparison
Calculate a threshold that minimize the FRR with Neymann-Pearson criterion with a priori FAR=10^-3.
Argenti’s filter has a significative lower FRR for Samsung and Olympus.
In the general the two filters show a comparable behavior.
Mihçak filter: 99.09%
Argenti filter: 96.61%
Low Pass filter: 84.44%
Ten to the minus three
The LP filter has the worst behaviour as obviously expected.
The other two filters showed a comparable behaviour: the FRR has the same order of magnitude
Argenti’s filter has a significative lower FRR for Samsung and Olympus
In the other case does not exhibit a considerable improvement in the results
Because the filter depends on the reliability of the parameters estimation

14. Results- denoising filter comparison
Correlation values for 20 images from a Olympus FE120 with 5 fingerprints.
LP filter
Mihcak filter
Argenti filter
Mihçak filter: 99.09%
Argenti filter: 96.61%
Low Pass filter: 84.44%
LP filter
Mihcak filter
Argenti filter
The higher values are those related to the correlation between the noise residual of the Olympus FE120 images and its fingerprint.
The distributions of the correlation values are well separated in the Argenti cases.
Correlation values for 20 images from a Olympus FE120 with 5 fingerprints of various cameras are pictured FOR Mihcak (left) and Argenti (right) filters respectively
included

15. Conclusions Future Trends
Introducing a novel filter for the estimation of PRNU.
An analysis on different kinds of denoising filters for PRNU extraction as been presented.
Experimental results on camera identification have been provided.
Future Trends
Improve methodology extraction for PRNU.
Force parameter in the Argenti noise model and repeat the experiments.
In the end I show you a method for the source idetinfication that use Sensor Noise to determine what Cam Shot the images.The future trends are:
Overlap
Introdotto un nuovo filtro usato in un’altra area di ricerca
Modello paragonabile a quello del miodello di acquisizione
Impove parametrs estimation of the Argenti filter
Alpha lo calcoliamo dall’immagine che diamo in pasto alla procedure di stima
In particular provare a forzare alpha=1 (estremo dell’intervallo dei valori) in modo da far coincidere i modelli, vedere se I risultati milgiorano

16. Thank you

17. Argenti’s Filter signal-dependent noise model.
MMSE (minimum mean-square error) filter in undecimated wavelet domain.
noise free image
noise image
stationary zero-mean uncorrelated random process
electronics noise (AWGN)
The parameters to be estimated are: and
MMSE filter in spatial domain
On homogeneous pixels, log scatter plot-regression line
To extract the PRNU (fingerprint) we generally used denoising filtering in particulary in our analysis we have compare:
A basic low pass filter, used like lower bound performance
A mihcak Filter
A Argenti-Alparone Filter
All of this are filter based on Wavelet domain and different noise model. The assumption to apply our techniques is to have a camera available or N images taken by the camera
This noise model coincide with the digital camera sensor output model with

18. Digital Camera Identification
Acquisition Process
-CFA: Bayer pattern (GRGB)
-sensor: CCD, CMOS
-Digital Image Processor: interpolation, white balancing, gamma correction, noise reduction
-JPEG compression
Fingerprint from:
Lens Aberration
Color Filter Array and Demosaicking
Sensor imperfections
Features (color, IQM, BSM, HOWS)‏
To better understand what a digital fingerprint means now let’s briefly analize the acquistion process within a digital camera.
It’s possible to see different moduls and each of this leaves a sort of modification that can be use for our scope. In particular for our scope it’s important the ccd sensor that because of its intrinsic disomohegenties generates a specific kind of noise the PRNU;

19. Sensor fingerprint & PRNU
Sensor imperfections
defective pixels: hot/dead pixels (removed by post-processing)‏
shot noise (random)
pattern noise (systematic):
Fixed Pattern Noise: dark current (exposure, temperature) suppressed by subtracting a dark frame from the image.
Photo Response Non-Uniformity: inhomogenities over the silicon wafer and imperfections generated during sensor manufacturing process (flat fielding)
Crypto: il digest è legato strettamente al contenuto e viene definito un particolare formato e non è possibile usarne altri; per ogni midifca fatta sull’immagine il digest cambia.
Properties PRNU:
unique for each sensor
multiplicative noise

20. Digital Camera Model noisy image noise free image PRNU random noise
denoising filter
Additive-multiplicative relation
Argenti’s filter model
To extract the PRNU (fingerprint) we generally used denoising filtering in particulary in our analysis we have compare:
A basic low pass filter, used like lower bound performance
A mihcak Filter
A Argenti-Alparone Filter
All of this are filter based on Wavelet domain and different noise model. The assumption to apply our techniques is to have a camera available or N images taken by the camera
Equal when

21. Argenti’s Filter signal-dependent noise model
noise free image
noise image
stationary zero-mean uncorrelated random process
electronics noise (AWGN)
This noise model coincides with the digital camera sensor output model
when
Minimizzazione lineare locale errore quadratico medio

22. Digital Camera Identification
fingerprint estimation
taken by the same camera A
This is the process to exctract fingerprint first we take N images from a camera and for each image we apply the selected Denoising Filter to obtain DEnoised Images; next we subtracting from each Noisy Image the respective Denoised one to get the PRNU and finally averaging them we get the FingerPrint of the camera.
PRNU
camera A